This is an up-dated fish file with only the “final” fish multivariate analyses for the Flagstone MS.
For data processing:
NOTE: Transects with visibility < 2 m were deleted from the analysis.
NOTE: These analyses include 2015.
DATA TRANSFORM: none
I set the data transform to ‘none’ because there wasn’t a lot of variation in spp density among groups. Some but not a lot. Again, this emphasizes the more abudnant species, which is, I think, ok.
NOTES
Because there were a lot of zeros for some species, we lumped some species or taxa into higher taxonomic categories. Species higher grouping used in the analyses are:
## Spp_Code Spp_Group Common_Name
## 1 BAITBALL BAIT bait-sardines-anchovy
## 4 SEBYTy SEBYTy yellowtail-black yoy
## 5 SEME SEME black rockfish
## 8 SEMY SEMY blue rockfish
## 12 SCMA SCMA cabezon
## 14 SEPIy SEPIy canary rockfish YOY
## 15 SENE SENE china rockfish
## 17 SECA SECA copper rockfish
## 20 HEXA HEXA Hexagrammos spp
## 23 CLUP BAIT Herring
## 25 HEDE HEXA Kelp Greenling
## 26 BRFR EMBI Kelp Surfperch
## 29 OPEL OPEL Lingcod
## 34 ENMO BAIT Northern Anchovy
## 38 SASA BAIT pacific sardine
## 39 OXPI HEXA Painted Greenling
## 41 RHVA EMBI Pile perch
## 45 HEHE HEXA Red Irish Lord
## 47 HELA HEXA Rock Greenling
## 49 RYOY RYOY rockfish_yoy
## 55 CYAG EMBI Shiner Surfperch
## 60 EMLA EMBI Striped Surfperch
## 61 EMBI EMBI Surfperches
## 63 AUFL AUFL Tubesnout
## 66 HEST HEXA Whitespotted greenling
## 69 SECAy SECAy Copper rockfish YOY
## 70 SEMEy SEMEy black rockfish YOY
This list may have been sub-setted again. See below.
No text here. Just calculating averages by year x site x depth and outputting data.
No text here. Just calculating averages by year x site and outputting data.
The ordinations here use transect level information in constrained ordinations. The PerMANOVA uses Year, Site, and Depth as Factors. The ordination is and RDA-type approach to Canonical Analysis of Principal Coordinates (not exactly the same) using the ‘capscale’ package. I present centroids and se, not transects in the figures for clarity.
Note: This analysis does NOT include rockfish YOY. We deleted YOY from the analyses because they were highly variable and often appeared in only one year. Univariate plots do show YOY.
DATA TRANSFORM: none
Taxa Included: OPEL, HEXA, EMBI, AUFL, BAIT, SECA, SCMA, SENE, SEME
In the PerMANVOA everything is significant. I think this is OK. Things are messy. I don’t actually think it is necessary for our paper.
The associated ordination is more clear. This ordination uses site x year x depth as groups and transects as replicates. I have, however, calculated the centroids for plotting.
FYI removing BAIT doesn’t do much.
Bray-Curtis transform.
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## SITE 4 14.493 3.6234 24.959 0.11590 0.001 ***
## ZONE 1 8.465 8.4649 58.309 0.06769 0.001 ***
## YEAR 1 3.378 3.3776 23.266 0.02701 0.001 ***
## SITE:ZONE 4 4.285 1.0712 7.379 0.03426 0.001 ***
## SITE:YEAR 4 5.641 1.4103 9.715 0.04511 0.001 ***
## ZONE:YEAR 1 2.365 2.3647 16.289 0.01891 0.001 ***
## SITE:ZONE:YEAR 4 2.225 0.5563 3.832 0.01780 0.001 ***
## Residuals 580 84.200 0.1452 0.67332
## Total 599 125.052 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
To match PerMANOVA. RDA type analysis with permutations. This analysis has the same Factor groupings as the PerMANOVA.
Ordination plot for CAP based on site x year x depth. Error bars are +/- 1.0 s.e. Lower-right pane zooms in on the species scores.
Ordination plot for CAP based on site x year x depth. Error bars are +/- 1.0 s.e. BAIT is not shown but is to the lower right outside the current axes limits.
Same data replotted on one pane. We can see some clear site differences:
Groupings used in the Invert Ordination were:
bivalve, blood_star, brood_sea_star, Cal_cuc, chiton, crabs, green_urchin, hermit_crabs, kelp_crab, large_anemone, large_barnacle, large_nudibranch, large_sea_star, leather_star, med_nudibranch, med_sea_star, orange_cucumber, P_ochraceous, purple_urchin, red_urchin, sea_cucumber, sea_star_YOY, shelled_gastropod, shelled_mollusk, small_anemone, sponge, tunicate
The invert ordination was pretty clear. Tatoosh, for exmaple, was characterized by large numbers of all urchins. Destruction, Alava, and Tatoosh all had some Pisaster and leather stars.
Ordination plot for CAP based on site x year x depth. Error bars are +/- 1.0 s.e.
I used PCA for the habitat variable ordinations instead of a constrained ordination or nMDS in order to do data reduction and produce variables to include in future ordination or other analyzes (see below). Constrained ordinations are not really data reductions and nMDS doesn’t really produces usable axes. Thus PCA seemed the best approach.
PC1 mostly distinguishes between bedrock and boulder. Boulder areas had higher relief (>2m) and higher relief diversity. PC2 tends to distinguish between the two mid-complexity categories.
While there is some variation among years within sites (spread), it isn’t bad. Mostly, there are some obvious differences among sites with regard to habitat.
This might be a good initial ordination figure just describing the sites and showing that the physcial habitat differs among sites. Depths to a lesser extent.
Substrate PCA. Open circles are 5-m depth zone; closed circles 10-m depth zone.
Substrate PCA. Open circles are 5-m depth zone; closed circles 10-m depth zone.
The kelp ordination isn’t great and we can’t really reduce the axes much more than the four axes in the original data. Also, given only four groups and three actual species, I think it is better to maintain the identity and use the original data not PCs in the following analyses.
You can see some depth pattern with deeper sites (filled circles) off to the upper left. This is hardly surprising.
There is some separation of points based on site:
UPC PCA analysis. Open circles are 5-m depth zone; closed circles 10-m depth zone.
UPC PCA analysis. Open circles are 5-m depth zone; closed circles 10-m depth zone.
I combined the PCs from the habitat analysis with the kelp data. I then ran and RDA style constrained analysis.
This constrained habitat vs. fish analysis is ugly. In fact, it is non-significant! So while there are differences among sites x depth x year in fish abundance, they don’t seem related to habitat directly.
This actually makes sense when you compare the separate fish and habitat ordinations. For example, Cape Alava and Cape Johnson have different fish fauna, but similar habitat.
## [1] "YEAR" "SITE" "ZONE" "Subs_1" "Subs_2" "upc_1" "upc_2" "MACPYR"
## [9] "NERLUE" "PTECAL" "OTHER"
## Call: capscale(formula = cap_data[, cap.fish] ~ Subs_1 + Subs_1 + upc_1
## + upc_2 + MACPYR + NERLUE + PTECAL + OTHER, data = cap_data)
##
## Inertia Proportion Rank
## Total 1299.9421 1.0000
## Constrained 175.1500 0.1347 7
## Unconstrained 1124.7921 0.8653 9
## Inertia is mean squared Euclidean distance
## Species scores projected from '[' 'cap_data' '' 'cap.fish'
##
## Eigenvalues for constrained axes:
## CAP1 CAP2 CAP3 CAP4 CAP5 CAP6 CAP7
## 161.57 11.68 1.36 0.47 0.05 0.02 0.00
##
## Eigenvalues for unconstrained axes:
## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9
## 1078.7 39.3 4.9 1.2 0.3 0.2 0.1 0.1 0.0
## Permutation test for capscale under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: capscale(formula = cap_data[, cap.fish] ~ Subs_1 + Subs_1 + upc_1 + upc_2 + MACPYR + NERLUE + PTECAL + OTHER, data = cap_data)
## Df Variance F Pr(>F)
## Model 7 175.15 0.6229 0.699
## Residual 28 1124.79
## INVERTS
## Call: capscale(formula = cap_data_inverts[, cap.inverts] ~ Subs_1 +
## Subs_1 + upc_1 + upc_2 + MACPYR + NERLUE + PTECAL + OTHER, data =
## cap_data_inverts)
##
## Inertia Proportion Rank
## Total 1.5497 1.0000
## Constrained 0.6785 0.4378 7
## Unconstrained 0.8712 0.5622 27
## Inertia is mean squared Euclidean distance
## Species scores projected from '[' 'cap_data_inverts' '' 'cap.inverts'
##
## Eigenvalues for constrained axes:
## CAP1 CAP2 CAP3 CAP4 CAP5 CAP6 CAP7
## 0.6299 0.0349 0.0063 0.0036 0.0023 0.0012 0.0004
##
## Eigenvalues for unconstrained axes:
## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
## 0.5158 0.2907 0.0253 0.0127 0.0091 0.0063 0.0056 0.0023
## (Showing 8 of 27 unconstrained eigenvalues)
## Permutation test for capscale under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: capscale(formula = cap_data_inverts[, cap.inverts] ~ Subs_1 + Subs_1 + upc_1 + upc_2 + MACPYR + NERLUE + PTECAL + OTHER, data = cap_data_inverts)
## Df Variance F Pr(>F)
## Model 7 0.67852 3.4492 0.002 **
## Residual 31 0.87119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The Invert vs Habitat relationship was significant. The main difference separated Tatoosh from other areas. Interestingly, * Urchins in general were both positively and negatively associated with Nero.
* On Axis 1 Nero, Ptero and urchins are all off to the right. * On Axis 2 the urchins are positive while the kelps are more negative.
The postive relationship between UPC_1 and urchins in general puts the urchins at sites with more brown algae and less red algae.
The positive relationship with Subs_1 on the x-axis suggests more urchins at areas with bedrock.
Ordination of invertebrate density vs. habitat charcteristics. Note, the second pane zooms in on the central cluster of points and exclues some points seen in the first pane.
These plots include all the “groups” in the data. These taxa-groups are not all in the ordinations above. They are plotted here for referecne
Fish abundance by site and year.
Fish abundance by site and year.
Two heat plots for rockfish YOY. They are the same data, just ordered differently to emphasize either species or site.
The first heat plot shows clearly the different recruiment patterns among species with * Blacks and Black/YT recruiting heavily in 2016 * Yelloweye (SEPI) in 2018 * Copper/quill and unidentified RYOY in 2019.
NOTE: colors are scaled across rows.
Heat-map plot of YOY abundance
Heat-map plot of YOY abundance
Fish abundance by site and year.
Fish abundance by site and year.
Fish abundance by site and year.
Fish abundance by site and year.